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ComputeLRTrainingStdThroughMkl.ComputeStandardDeviation Method

  • Reference

Definition

Namespace:
Microsoft.ML.Trainers
Assembly:
Microsoft.ML.Mkl.Components.dll
Package:
Microsoft.ML.Mkl.Components v3.0.1
Package:
Microsoft.ML.Mkl.Components v1.0.0
Package:
Microsoft.ML.Mkl.Components v1.1.0
Package:
Microsoft.ML.Mkl.Components v1.2.0
Package:
Microsoft.ML.Mkl.Components v1.3.1
Package:
Microsoft.ML.Mkl.Components v1.4.0
Package:
Microsoft.ML.Mkl.Components v1.5.5
Package:
Microsoft.ML.Mkl.Components v1.6.0
Package:
Microsoft.ML.Mkl.Components v1.7.0
Package:
Microsoft.ML.Mkl.Components v2.0.0

Computes the standart deviation matrix of each of the non-zero training weights, needed to calculate further the standart deviation, p-value and z-Score.

public override Microsoft.ML.Data.VBuffer<float> ComputeStandardDeviation (double[] hessian, int[] weightIndices, int numSelectedParams, int currentWeightsCount, Microsoft.ML.Runtime.IChannel ch, float l2Weight);
override this.ComputeStandardDeviation : double[] * int[] * int * int * Microsoft.ML.Runtime.IChannel * single -> Microsoft.ML.Data.VBuffer<single>
Public Overrides Function ComputeStandardDeviation (hessian As Double(), weightIndices As Integer(), numSelectedParams As Integer, currentWeightsCount As Integer, ch As IChannel, l2Weight As Single) As VBuffer(Of Single)

Parameters

hessian
Double[]
weightIndices
Int32[]
numSelectedParams
Int32
currentWeightsCount
Int32
ch
IChannel

The IChannel used for messaging.

l2Weight
Single

The L2Weight used for training. (Supply the same one that got used during training.)

Returns

VBuffer<Single>

Applies to